{"id":"https://openalex.org/W2492220238","doi":"https://doi.org/10.1109/icc.2016.7511400","title":"Optimal energy efficient level set estimation of spatially-temporally correlated random fields","display_name":"Optimal energy efficient level set estimation of spatially-temporally correlated random fields","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2492220238","doi":"https://doi.org/10.1109/icc.2016.7511400","mag":"2492220238"},"language":"en","primary_location":{"id":"doi:10.1109/icc.2016.7511400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2016.7511400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001921003","display_name":"Zuoen Wang","orcid":"https://orcid.org/0000-0001-5069-5726"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zuoen Wang","raw_affiliation_strings":["Department of Electrical Engineering, University of Arkansas, Fayetteville, AR, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Arkansas, Fayetteville, AR, USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086922409","display_name":"Jingxian Wu","orcid":"https://orcid.org/0000-0003-1167-6930"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingxian Wu","raw_affiliation_strings":["Department of Electrical Engineering, University of Arkansas, Fayetteville, AR, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Arkansas, Fayetteville, AR, USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645589","display_name":"Jing Yang","orcid":"https://orcid.org/0000-0002-6009-864X"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Yang","raw_affiliation_strings":["Department of Electrical Engineering, University of Arkansas, Fayetteville, AR, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Arkansas, Fayetteville, AR, USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011665213","display_name":"Hai Lin","orcid":"https://orcid.org/0000-0002-6364-348X"},"institutions":[{"id":"https://openalex.org/I15807432","display_name":"Osaka Prefecture University","ror":"https://ror.org/02cf1je33","country_code":"JP","type":"education","lineage":["https://openalex.org/I15807432"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hai Lin","raw_affiliation_strings":["Osaka Furitsu Daigaku, Sakai, Osaka, JP"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka Furitsu Daigaku, Sakai, Osaka, JP","institution_ids":["https://openalex.org/I15807432"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3883,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.59026278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.6524717211723328},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5913047790527344},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48667198419570923},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4753781855106354},{"id":"https://openalex.org/keywords/stochastic-process","display_name":"Stochastic process","score":0.4557707607746124},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.44756877422332764},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.41959431767463684},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4146060347557068},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.39852532744407654},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2937888205051422},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07826399803161621}],"concepts":[{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.6524717211723328},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5913047790527344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48667198419570923},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4753781855106354},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.4557707607746124},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.44756877422332764},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.41959431767463684},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4146060347557068},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39852532744407654},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2937888205051422},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07826399803161621},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc.2016.7511400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2016.7511400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W143311957","https://openalex.org/W1530718350","https://openalex.org/W1940253356","https://openalex.org/W1969039219","https://openalex.org/W1999824796","https://openalex.org/W2011113174","https://openalex.org/W2075308182","https://openalex.org/W2097464240","https://openalex.org/W2097671030","https://openalex.org/W2099545273","https://openalex.org/W2127949150","https://openalex.org/W2150397642","https://openalex.org/W2152670498","https://openalex.org/W3102398676","https://openalex.org/W3102800692","https://openalex.org/W6605838369","https://openalex.org/W6631546570","https://openalex.org/W6785737950"],"related_works":["https://openalex.org/W4285599464","https://openalex.org/W3008047311","https://openalex.org/W2893341095","https://openalex.org/W3214840295","https://openalex.org/W2027111064","https://openalex.org/W2095860056","https://openalex.org/W1668360133","https://openalex.org/W2351359734","https://openalex.org/W2122669027","https://openalex.org/W2789353941"],"abstract_inverted_index":{"Level":[0],"set":[1],"estimation":[2],"(LSE)":[3],"is":[4,60,156],"the":[5,9,12,41,42,67,74,80,101,106,110,113,128,139,145,170,176,180,183,188,192,196,202,212],"process":[6,115],"of":[7,14,27,45,66,100,109,147,162,165,191],"classifying":[8],"region(s)":[10],"that":[11,50,125,209],"values":[13],"an":[15,121,159],"unknown":[16],"function":[17,161],"exceed":[18],"a":[19,24,46,84,93,163],"certain":[20],"threshold.":[21],"It":[22],"has":[23],"wide":[25],"range":[26],"applications":[28],"such":[29,168],"as":[30,158,169],"spectrum":[31],"sensing":[32],"or":[33],"environment":[34],"monitoring.":[35],"In":[36],"this":[37],"paper,":[38],"we":[39,118,199],"study":[40],"optimal":[43,122],"LSE":[44,99,123,129,141,153,213],"linear":[47,57],"random":[48,70,102,193],"field":[49,71,103],"changes":[51],"with":[52,144],"respect":[53],"to":[54,62,137],"time.":[55],"A":[56],"sensor":[58],"network":[59],"used":[61,136],"take":[63],"discrete":[64],"samples":[65,89],"spatially-temporally":[68],"correlated":[69],"in":[72,179],"both":[73],"space":[75],"and":[76,79,187,206],"time":[77,181],"domain,":[78,182],"sensors":[81],"operate":[82],"under":[83],"total":[85],"power":[86],"constraint.":[87],"The":[88,132,151],"are":[90,134],"congregated":[91],"at":[92],"fusion":[94],"center":[95],"(FC),":[96],"which":[97],"performs":[98],"by":[104],"using":[105],"noisy":[107],"observation":[108],"samples.":[111],"Under":[112],"Gaussian":[114],"(GP)":[116],"framework,":[117],"first":[119],"develop":[120],"algorithm":[124],"can":[126,200,210],"minimize":[127,211],"error":[130,142,154,214],"probability.":[131,215],"results":[133],"then":[135],"derive":[138],"exact":[140],"probability":[143,155],"assistance":[146],"frequency":[148],"domain":[149],"analysis.":[150],"analytical":[152,197],"expressed":[157],"explicit":[160],"number":[164],"system":[166],"parameters,":[167],"distance":[171,205],"between":[172],"two":[173],"adjacent":[174],"nodes,":[175],"sampling":[177,207],"period":[178,208],"signal-to-noise":[184],"ratio":[185],"(SNR),":[186],"spatial-temporal":[189],"correlation":[190],"field.":[194],"With":[195],"results,":[198],"identify":[201],"optimum":[203],"node":[204]},"counts_by_year":[{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}